Recherche Images Maps Play YouTube Actualités Gmail Drive Plus »
Connexion
Les utilisateurs de lecteurs d'écran peuvent cliquer sur ce lien pour activer le mode d'accessibilité. Celui-ci propose les mêmes fonctionnalités principales, mais il est optimisé pour votre lecteur d'écran.

Brevets

  1. Recherche avancée dans les brevets
Numéro de publicationUS20030125988 A1
Type de publicationDemande
Numéro de demandeUS 10/287,329
Date de publication3 juil. 2003
Date de dépôt4 nov. 2002
Date de priorité2 nov. 2001
Autre référence de publicationCA2464374A1, CA2464613A1, CA2465531A1, CA2465533A1, CA2465702A1, CA2465706A1, CA2465712A1, CA2465725A1, CA2465760A1, CN1582443A, CN1613068A, CN1613069A, CN1613070A, CN1613086A, CN1613087A, CN1613088A, CN1636210A, CN100449531C, EP1440385A2, EP1440387A2, EP1440388A2, EP1440389A2, EP1440390A2, EP1440409A2, EP1440410A2, EP1440412A2, EP1442415A2, US7181375, US7617078, US7711404, US7744540, US7917377, US8214224, US8214225, US8280750, US8626533, US8949079, US9165116, US20030120133, US20030120134, US20030120458, US20030120514, US20030125984, US20030125985, US20030126101, US20030130871, US20050159654, US20090259487, US20100222646, US20150100352, WO2003040878A2, WO2003040878A8, WO2003040879A2, WO2003040879A3, WO2003040964A2, WO2003040964A3, WO2003040965A2, WO2003040965A3, WO2003040966A2, WO2003040966A8, WO2003040987A2, WO2003040987A3, WO2003040987A8, WO2003040988A2, WO2003040988A3, WO2003040989A2, WO2003040989A3, WO2003040990A2, WO2003040990A3
Numéro de publication10287329, 287329, US 2003/0125988 A1, US 2003/125988 A1, US 20030125988 A1, US 20030125988A1, US 2003125988 A1, US 2003125988A1, US-A1-20030125988, US-A1-2003125988, US2003/0125988A1, US2003/125988A1, US20030125988 A1, US20030125988A1, US2003125988 A1, US2003125988A1
InventeursR. Rao, Sathyakama Sandilya
Cessionnaire d'origineRao R. Bharat, Sathyakama Sandilya
Exporter la citationBiBTeX, EndNote, RefMan
Liens externes: USPTO, Cession USPTO, Espacenet
Patient data mining with population-based analysis
US 20030125988 A1
Résumé
A system and method for analyzing population-based patient information is provided. The method includes the steps of data mining a plurality of patient records using a domain knowledge base relating to a disease of interest; compiling the mined data into a plurality of structured patient records; inputting at least one patient criteria relating to the disease of interest; and extracting at least one structured patient record matching the at least one patient criteria. The system includes a data miner for mining information from the plurality of patient records using a domain knowledge base relating to a disease of interest and for compiling the mined data into a plurality of structured patient records; an interface for inputting at least one patient criteria relating to the disease of interest; and a processor adapted for extracting at least one of the structured patient records matching the at least one patient criteria.
Images(6)
Previous page
Next page
Revendications(20)
What is claimed is:
1. A method for analyzing patient records, the method comprising the steps of:
data mining a plurality of patient records using a domain knowledge base relating to a disease of interest;
compiling the mined data into a plurality of structured patient records;
inputting at least one patient criteria relating to the disease of interest; and
extracting at least one structured patient record matching the at least one patient criteria.
2. The method as in claim 1, further comprising the step of determining a patient outcome of the at least one structured patient record.
3. The method as in claim 2, further comprising the steps of changing a value of the at least one patient criteria and repeating the extracting and determining steps.
4. The method as in claim 1, wherein the plurality of patient records are stored in structured and unstructured sources.
5. The method as in claim 1, wherein the plurality of patient records are collected over a course of treatment of a plurality of patients.
6. The method as in claim 2, further comprising the step of correlating a plurality of criteria to a plurality of patient outcomes.
7. The method as in claim 6, further comprising the step of suggesting a hypothesis for a clinical trial based on the correlation.
8. The method as in claim 7, further comprising the step of validating the hypothesis by performing a retrospective study on the plurality of structured patient records.
9. A system for analyzing a plurality of patient records, the plurality of patient records being stored in structured and unstructured sources, the system comprising:
a data miner for mining information from the plurality of patient records using a domain knowledge base relating to a disease of interest and for compiling the mined data into a plurality of structured patient records;
an interface for inputting at least one patient criteria relating to the disease of interest; and
a processor adapted for extracting at least one of the structured patient records matching the at least one patient criteria.
10. The system as in claim 9, further comprising a database for storing the plurality of structured patient records.
11. The system as in claim 9, wherein the processor is further adapted to determine a patient outcome of the at least one structured patient record.
12. The system as in claim 11, wherein the processor is further adapted to correlate a plurality of criteria to a plurality of patient outcomes.
13. The system as in claim 12, wherein the processor is further adapted to suggest a hypothesis for a clinical trial based on the correlation.
14. A method for conducting a retrospective study on a plurality of patient records, the method comprising the steps of:
data mining the plurality of patient records using a domain knowledge base relating to a disease of interest;
compiling the mined data into a plurality of structured patient records;
inputting a plurality of patient criteria forming a hypothesis relating to the disease of interest; and
extracting a plurality of structured patient records matching the plurality of patient criteria.
15. The method as in claim 14, further comprising the step of determining patient outcomes from the plurality of structured patient records.
16. The method as in claim 15, further comprising the step of validating the hypothesis by comparing the patient outcomes to a suggested outcome.
17. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for analyzing patient records, the method steps comprising:
data mining a plurality of patient records using a domain knowledge base relating to a disease of interest;
compiling the mined data into a plurality of structured patient records;
inputting at least one patient criteria relating to the disease of interest; and
extracting at least one structured patient record matching the at least one patient criteria.
18. The program storage device as in claim 17, further comprising the method step of determining a patient outcome of the at least one structured patient record.
19. The program storage device as in claim 18, further comprising the method steps of changing a value of the at least one patient criteria and repeating the extracting and determining steps.
20. The program storage device as in claim 17, wherein the plurality of patient records are stored in structured and unstructured sources.
Description
    CROSS REFERENCE TO RELATED APPLICATIONS
  • [0001]
    This application claims the benefit of U.S. Provisional Application Serial No. 60/335,542, filed on Nov. 2, 2001, which is incorporated by reference herein in its entirety.
  • FIELD OF THE INVENTION
  • [0002]
    The present invention relates to medical information processing systems, and, more particularly to a computerized system and method for performing outcome analysis on a patient based on population-based information derived from various sources and for conducting retrospective studies on the population-based information.
  • BACKGROUND OF THE INVENTION
  • [0003]
    The proper care of medical patients is essential for optimal treatment of their medical conditions. Typically, a patient having a particular condition/ailment is prescribed a medicine or treatment based upon established treatment guidelines. The treatment guidelines outline, inter alia, the specific dosages of medicines, the frequency in which dosages should be administered, instructions on how dosages should be administered and the time-lines for therapeutic treatments. Oftentimes, treatment for new patients is administered directly from the treatment guidelines with little variation. These guidelines are typically derived through prospective medical studies. Prospective medical studies, namely, randomized clinical trials, are studies wherein researches empirically test hypothesis in near ideal conditions by screening the patient population, ensuring that patient care diligently follows the guidelines and recording all relevant data. Such practices fail to take full advantages of historical medical data, rather, relying only on success rates for the patients that rigidly adhered to the treatment guidelines. Additionally, clinical trials are very expensive to conduct.
  • [0004]
    Historical medical data represents a valuable source in the analysis of the patient care process and medical outcomes. As indicated, treatment guidelines have been generated based solely upon the results of treatment on patients who rigidly adhered to the treatment guidelines. However, the number of variables from patient and professional medical care having an impact on the results of patient care is exceedingly high. Moreover, the relationship between these variables is virtually unknown. Accordingly, the ability to fully learn from past medical data could greatly improved patient health care.
  • [0005]
    Retrospective studies, for example, the analysis of historical medical patient records from a hospital, are complementary to prospective clinical trials. Health-care organizations are accumulating vast stores of patient data, which are a vital tool for knowledge management. Analyzing this already-collected information may lead to insights that can be subsequently verified in a prospective trial. Most importantly, retrospective studies can measure, in a least two ways, the impact of guidelines in real-life clinical settings. First, retrospective studies can determine the effectiveness of the treatment for a patient population that was excluded from clinical trials. For example, patients above 65, or those with other diseases may be excluded in a clinical trial—however, the guideline validated in that trial is now used to treat all hospital patients. Second, patient treatment in a hospital may differ from that in a trial. For instance, the colon cancer guideline mandates commencing chemotherapy within 6 weeks of surgery, which is rigorously enforced in the clinical trial. However, in a hospital, some patients may begin chemotherapy up to 10 weeks after surgery (e.g., they may be too sick or miss appointments). The impact of this delay on a patient's outcome can only be determined via retrospective analysis since it is not ethical to conduct a clinical trial that would test the impact of this delay—in effect, withholding the accepted standard of care.
  • [0006]
    However, analyzing hospital data is hard for many reasons. First, medical data is very complex to analyze because of its rich structure. Many traditional statistical methods are ill-suited to data with structure, time-sequenced events (medical data has important temporal components) and/or no structure such as free text, images, etc. Second, because the hospital patient data was collected to treat the patient (as opposed to collected for analysis in a clinical trial), it is imperfect in many ways, for example, missing/incorrect/inconsistent data; key outcomes/variables not recorded; bias in data collection, e.g., sick patients get more tests than well ones, (this is perfectly natural from the medical point of view, but has inherent assumptions that may cause problems for many algorithms); and variables collected/treatments change over time, which particularly impacts some long-term diseases whose treatment can span decades. Lastly, there is wide variation in practice among medical professionals determining if a patient is on a guideline and treated properly is difficult to tell.
  • [0007]
    In view of the above, there exists a need for techniques to collect population-based patient information from a variety of sources, to perform outcome analysis on the collected information, and to conduct retrospective analysis on a large quantity of medical information derived from various sources in a rapid manner.
  • SUMMARY OF THE INVENTION
  • [0008]
    A system and method for analyzing population-based patient information is provided.
  • [0009]
    According to one aspect of the present invention, a method for analyzing patient records is provided including the steps of data mining a plurality of patient records using a domain knowledge base relating to a disease of interest; compiling the mined data into a plurality of structured patient records; inputting at least one patient criteria relating to the disease of interest; and extracting at least one structured patient record matching at least one patient criteria.
  • [0010]
    According to another aspect of the present invention, a system for analyzing a plurality of patient records includes a data miner for mining information from the plurality of patient records using a domain knowledge base relating to a disease of interest and for compiling the mined data into a plurality of structured patient records; an interface for inputting at least one patient criteria relating to the disease of interest; and a processor adapted for extracting at least one of the structured patient records matching at least one patient criteria.
  • [0011]
    In a further aspect of the present invention, a method for conducting a retrospective study on a plurality of patient records is provided. The method includes the steps of data mining the plurality of patient records using a domain knowledge base relating to a disease of interest; compiling the mined data into a plurality of structured patient records; inputting a plurality of patient criteria forming a hypothesis relating to the disease of interest; and extracting a plurality of structured patient records matching the plurality of patient criteria. The method further includes the steps of determining patient outcomes from the plurality of structured patient records and validating the hypothesis by comparing the patient outcomes to a suggested outcome.
  • [0012]
    In another aspect of the present invention, a program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for analyzing patient records is provided including the method steps of data mining a plurality of patient records using a domain knowledge base relating to a disease of interest; compiling the mined data into a plurality of structured patient records; inputting at least one patient criteria relating to the disease of interest; and extracting at least one structured patient record matching the at least one patient criteria.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • [0013]
    The above and other aspects, features and advantages of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which:
  • [0014]
    [0014]FIG. 1 is a block diagram of a computer processing system to which the present invention may be applied according to an embodiment of the present invention;
  • [0015]
    [0015]FIG. 2 illustrates an exemplary computerized patient record (CPR); and
  • [0016]
    [0016]FIG. 3 illustrates an exemplary data mining framework for mining highquality structured medical information;
  • [0017]
    [0017]FIG. 4 illustrates a block diagram of an exemplary analysis system according to an embodiment of the present invention; and
  • [0018]
    [0018]FIG. 5 illustrates a flow diagram for analyzing large amounts of medical information according to an embodiment of the present invention.
  • DESCRIPTION OF PREFERRED EMBODIMENTS
  • [0019]
    To facilitate a clear understanding of the present invention, illustrative examples are provided herein which describe certain aspects of the invention. However, it is to be appreciated that these illustrations are not meant to limit the scope of the invention, and are provided herein to illustrate certain concepts associated with the invention.
  • [0020]
    A system and method for analyzing population-based medical data is provided. According to an embodiment of the present invention, a computer-based system will compile population-based patient data from various sources, e.g., structured and unstructured, into a structured database for analysis. First, the system will assimilate information from both structured, e.g., financial, and unstructured, e.g., imaging, sources within a computerized patient record (CPR). These data can be automatically extracted, combined, and analyzed in a meaningful way.
  • [0021]
    The present invention allows for analysis of a large amount of information in a rapid manner, as opposed to the traditional method of medical personnel reviewing each record and transposing their findings. Since information is collected from a variety of sources containing different information relating to specific patients, various criteria or variables can be analyzed to determine their effect on a proposed treatment or guideline.
  • [0022]
    It is to be understood that the present invention may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. Preferably, the present invention is implemented in software as a program tangibly embodied on a program storage device. The program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine is implemented on a computer platform having hardware such as one or more central processing units (CPU), a random access memory (RAM), and input/output (I/O) interface(s). The computer platform also includes an operating system and microinstruction code. The various processes and functions described herein may either be part of the microinstruction code or part of the program (or combination thereof) which is executed via the operating system. In addition, various other peripheral devices may be connected to the computer platform such as an additional data storage device and a printing device.
  • [0023]
    It is to be understood that, because some of the constituent system components and method steps depicted in the accompanying figures are preferably implemented in software, the actual connections between the system components (or the process steps) may differ depending upon the manner in which the present invention is programmed.
  • [0024]
    [0024]FIG. 1 is a block diagram of a computer processing system 100 to which the present invention may be applied according to an embodiment of the present invention. The system 100 includes at least one processor (hereinafter processor) 102 operatively coupled to other components via a system bus 104. A read-only memory (ROM) 106, a random access memory (RAM) 108, an I/O interface 110, a network interface 112, and external storage 114 are operatively coupled to the system bus 104. Various peripheral devices such as, for example, a display device, a disk storage device (e.g., a magnetic or optical disk storage device), a keyboard, and a mouse, may be operatively coupled to the system bus 104 by the I/O interface 110 or the network interface 112.
  • [0025]
    The computer system 100 may be a standalone system or be linked to a network via the network interface 112. The network interface 112 may be a hard-wired interface. However, in various exemplary embodiments, the network interface 112 can include any device suitable to transmit information to and from another device, such as a universal asynchronous receiver/transmitter (UART), a parallel digital interface, a software interface or any combination of known or later developed software and hardware. The network interface may be linked to various types of networks, including a local area network (LAN), a wide area network (WAN), an intranet, a virtual private network (VPN), and the Internet.
  • [0026]
    The external storage 114 may be implemented using a database management system (DBMS) managed by the processor 102 and residing on a memory such as a hard disk. However, it should be appreciated that the external storage 114 may be implemented on one or more additional computer systems. For example, the external storage 114 may include a data warehouse system residing on a separate computer system.
  • [0027]
    Those skilled in the art will appreciate that other alternative computing environments may be used without departing from the spirit and scope of the present invention.
  • [0028]
    Increasingly, health care providers are employing automated techniques for information storage and retrieval. The use of a computerized patient record (CPR) to maintain patient information is one such example. As shown in FIG. 2, an exemplary CPR (200) includes information that is collected over the course of a patient's treatment. This information may include, for example, computed tomography (CT) images, X-ray images, laboratory test results, doctor progress notes, details about medical procedures, prescription drug information, radiological reports, other specialist reports, demographic information, and billing (financial) information.
  • [0029]
    A CPR typically draws from a plurality of data sources, each of which typically reflects a different aspect of a patient's care. Structured data sources, such as financial, laboratory, and pharmacy databases, generally maintain patient information in database tables. Information may also be stored in unstructured data sources, such as, for example, free text, images, and waveforms. Often, key clinical findings are only stored within physician reports, e.g., dictations.
  • [0030]
    [0030]FIG. 3 illustrates an exemplary data mining system for mining high-quality structured clinical information using data mining techniques described in “Patient Data Mining,” by Rao et al., copending U.S. patent application Ser. No. ______, (Attorney Docket No. 8706-600) filed herewith, which is incorporated by reference in its entirety. The data mining system includes a data miner (350) that mines information from a CPR (310) using domain-specific knowledge contained in a knowledge base (330). The data miner (350) includes components for extracting information from the CPR (352), combining all available evidence in a principled fashion over time (354), and drawing inferences from this combination process (356). The mined information may be stored in a structured CPR database (380). In this manner, all information contained in a CPR, whether from a structured or unstructured source, will stored in a structured fashion.
  • [0031]
    The extraction component (352) deals with gleaning small pieces of information from each data source regarding a patient, which are represented as probabilistic assertions about the patient at a particular time. These probabilistic assertions are called elements. The combination component (354) combines all the elements that refer to the same variable at the same time period to form one unified probabilistic assertion regarding that variable. These unified probabilistic assertions are called factoids. The inference component (356) deals with the combination of these factoids, at the same point in time and/or at different points in time, to produce a coherent and concise picture of the progression of the patient's state over time. This progression of the patient's state is called a state sequence.
  • [0032]
    The present invention can build an individual model of the state of a patient. The patient state is simply a collection of variables or criteria that one may care about relating to the patient. The information of interest may include a state sequence, i.e., the value of the patient state at different points in time during the patient's treatment.
  • [0033]
    Each of the above components uses detailed knowledge regarding the domain of interest, such as, for example, a disease of interest. This domain knowledge base (330) can come in two forms. It can be encoded as an input to the system, or as programs that produce information that can be understood by the system. The part of the domain knowledge base (330) that is input to the present form of the system may also be learned from data.
  • [0034]
    As mentioned, the extraction component (352) takes information from the CPR (310) to produce probabilistic assertions (elements) about the patient that are relevant to an instant in time or time period. This process is carried out with the guidance of the domain knowledge that is contained in the domain knowledge base (330). The domain knowledge required for extraction is generally specific to each source.
  • [0035]
    Referring to FIG. 4, an exemplary analysis system 400 according to an embodiment of the present invention is illustrated. The analysis system 400 includes a processor 402 for extracting information from the structured database 380 and for performing different tasks on the extracted information. Additionally, the processor 402 is adapted to receive manually inputted patient criteria or variables 414 via an I/O interface which will be used to extract specific information from the database 380. Each task performed by the analysis system 200 is performed by an executable module residing either in the processor of the system 402 and/or in a memory device (e.g., RAM, ROM, external storage, etc.) of the system.
  • [0036]
    Referring to FIG. 5, a flow chart illustrating a method of analyzing population-based data is provided. For example, the problem of unsatisfactory outcomes (e.g., clinical, financial, and length of stay) in patients with diabetes who sustain a myocardial infarction can be examined for a particular hospital.
  • [0037]
    First, a plurality of computerized patient records is assembled during the course of treatment of a large number of patients over time, for example, in a particular hospital. This historical data is mined using a domain knowledge base relating to a disease of interest and compiled in a structured CPR database (step 502). For example, information is extract from a variety of sources to identify patients with a confirmed diagnosis of acute myocardial infarction (AMI). This will not be based on ICD-9 codes (which have about 90% accuracy), but on a combination of clinical, laboratory, and EKG findings that meet the MONICA criteria, the internationally accepted standard for identifying AMI patients.
  • [0038]
    One or more criteria or variables relating to the disease of interest is inputted into the system (step 504). The system extracts patient records from the structured database which conform to the criteria (step 506). For example, once the AMI patients are identified, the system will separate out a subset of patients with diabetes mellitus (e.g., the criteria), based on pharmacy data showing the need for administration of insulin or other anti-diabetic agents, and on lab data showing high blood sugars.
  • [0039]
    Then, the system determines patient outcomes for the extracted patient records (step 508) and outputs the results. At least one value of the patient criteria may be changed to determine how the change in value of the criteria effects the outcome (step 510). Finally, the system will compile and output the outcome results so the appropriate personnel can review (step 512). The system identifies differences in clinical outcomes, e.g. death, procedures (coronary bypass or angioplasty), infections, etc, and places these results in the context of the accompanying financial, case-mix, treatment, therapy and length of stay-data. The output may be a chart, table, curve, etc. illustrating the effects of the changes in criteria against patient outcomes.
  • [0040]
    In another embodiment, the system and method of the present invention will perform outcome analysis on a particular patient, for example, a physician may want to determine the best prescription drug for lowering a patient's cholesterol level. The system will extract patient records for patient with a cholesterol level over a predetermined limit, e.g., 250. The physician will enter criteria or variables 414 related to a current state of the patient, e.g., age, blood pressure, LDL cholesterol, HDL cholesterol, etc. The processor 402 will then interact with the structured CPR database 380 to extract patient records that match the criteria of the current patient and will output the patient outcomes versus drug treatments of the extracted records. The physician may change a value of one or more of the criteria or variables, e.g., use of a different drug, changes to the patients smoking habits, etc., to determine how the outcome is affected by the change, wherein the system will extract new patient records to reflect patient outcomes based on the new set of variables. Since the system can extract different patient records based on different criteria from a large volume of records, the system can perform outcome analysis much faster than in the traditional manner of trying to search by hand patient records with similar information.
  • [0041]
    Additionally, the system may be used to generate a hypothesis for a potential prospective clinical trial by correlating the inputted criteria to the determined outcomes.
  • [0042]
    In another embodiment, the system and method of the present invention may be employed to conduct a retrospective study. During a prospective clinical trial, a particular group of people, for example, males ages 25 to 40, may have been observed to determine the most appropriate guideline for treating a particular disease. The guideline developed from the clinical trial is later then applied to all age groups without further testing. The system and method of the present invention will allow a study to be conducted on people excluded from the trial by extracting patient records which match the guideline created during the actual trial but will be restricted by an inputted patient criteria, e.g., females ages 40-50. The system and method of the present invention allow a retrospective study be conducted on a large population of people without the need for someone to manually review a large number of records.
  • [0043]
    Furthermore, a retrospective study may be conducted to validate the hypothesis generated by correlating the inputted criteria to the determined patient outcomes and, then, comparing the determined patient outcomes to a suggested patient outcome of the hypothesis.
  • [0044]
    The analysis system and method of the present invention provides for a collection of a large volume of data from various sources, i.e., structured and unstructured, to be analyzed in an efficient and rapid manner. The method and system will provide improve quality of care by allowing medical professionals to perform patient outcome analysis on population-based patient information, e.g., a large quantity of patients treated by a hospital, to determine the most appropriate treatment. Additionally, the system and method of the present invention will reduce costs to researchers and hospitals by allowing retrospective studies to be performed automatically by mining data from varied sources, as opposed to conventional individual review and analysis.
  • [0045]
    Although illustrative embodiments of the present invention have been described herein with reference to the accompanying drawings, it is to be understood that the invention is not limited to those precise embodiments, and that various other changes and modifications may be affected therein by one skilled in the art without departing from the scope or spirit of the invention.
Citations de brevets
Brevet cité Date de dépôt Date de publication Déposant Titre
US567255 *12 sept. 18958 sept. 1896F TwoMichael p
US4946679 *29 sept. 19897 août 1990Thys Jacobs SusanMethod for the treatment of premenstrual syndrome
US5172418 *8 août 199015 déc. 1992Fuji Photo Film Co., Ltd.Image processing apparatus using disease-based image processing conditions
US5307262 *29 janv. 199226 avr. 1994Applied Medical Data, Inc.Patient data quality review method and system
US5359509 *31 oct. 199125 oct. 1994United Healthcare CorporationHealth care payment adjudication and review system
US5365425 *22 avr. 199315 nov. 1994The United States Of America As Represented By The Secretary Of The Air ForceMethod and system for measuring management effectiveness
US5508912 *25 juin 199016 avr. 1996Barry SchneidermanClinical database of classified out-patients for tracking primary care outcome
US5544044 *2 août 19916 août 1996United Healthcare CorporationMethod for evaluation of health care quality
US5557514 *23 juin 199417 sept. 1996Medicode, Inc.Method and system for generating statistically-based medical provider utilization profiles
US5652842 *1 mars 199429 juil. 1997Healthshare Technology, Inc.Analysis and reporting of performance of service providers
US5664109 *7 juin 19952 sept. 1997E-Systems, Inc.Method for extracting pre-defined data items from medical service records generated by health care providers
US5669877 *7 mars 199423 sept. 1997Sims Deltec, Inc.Systems and methods for automated testing of medical equipment
US5706441 *7 juin 19956 janv. 1998Cigna Health CorporationMethod and apparatus for objectively monitoring and assessing the performance of health-care providers
US5724379 *21 déc. 19943 mars 1998Healthchex, Inc.Method of modifying comparable health care services
US5738102 *31 juil. 199614 avr. 1998Lemelson; Jerome H.Patient monitoring system
US5811437 *22 avr. 199722 sept. 1998Eli Lilly And CompanyMethods of increasing nitric oxide synthesis
US5832450 *5 mai 19973 nov. 1998Scott & White Memorial HospitalElectronic medical record using text database
US5835897 *22 juin 199510 nov. 1998Symmetry Health Data SystemsComputer-implemented method for profiling medical claims
US5899998 *31 août 19954 mai 1999Medcard Systems, Inc.Method and system for maintaining and updating computerized medical records
US5908383 *17 sept. 19971 juin 1999Brynjestad; UlfKnowledge-based expert interactive system for pain
US5924073 *14 nov. 199513 juil. 1999Beacon Patient Physician Association, LlcSystem and method for assessing physician performance using robust multivariate techniques of statistical analysis
US5939528 *31 oct. 199717 août 1999Cornell Research Foundation, Inc.Crystalline FRAP complex
US5991731 *1 févr. 199923 nov. 1999University Of FloridaMethod and system for interactive prescription and distribution of prescriptions in conducting clinical studies
US6067466 *18 nov. 199823 mai 2000New England Medical Center Hospitals, Inc.Diagnostic tool using a predictive instrument
US6076088 *6 févr. 199713 juin 2000Paik; WoojinInformation extraction system and method using concept relation concept (CRC) triples
US6078894 *28 mars 199720 juin 2000Clawson; Jeffrey J.Method and system for evaluating the performance of emergency medical dispatchers
US6081786 *1 avr. 199927 juin 2000Triangle Pharmaceuticals, Inc.Systems, methods and computer program products for guiding the selection of therapeutic treatment regimens
US6083693 *14 juin 19964 juil. 2000Curagen CorporationIdentification and comparison of protein-protein interactions that occur in populations
US6108635 *30 avr. 199722 août 2000Interleukin Genetics, Inc.Integrated disease information system
US6125194 *4 févr. 199826 sept. 2000Caelum Research CorporationMethod and system for re-screening nodules in radiological images using multi-resolution processing, neural network, and image processing
US6128620 *2 févr. 19993 oct. 2000Lemed IncMedical database for litigation
US6139494 *15 oct. 199731 oct. 2000Health Informatics ToolsMethod and apparatus for an integrated clinical tele-informatics system
US6151581 *16 déc. 199721 nov. 2000Pulsegroup Inc.System for and method of collecting and populating a database with physician/patient data for processing to improve practice quality and healthcare delivery
US6196970 *22 mars 19996 mars 2001Stephen J. BrownResearch data collection and analysis
US6212519 *30 juin 19983 avr. 2001Simulconsult, Inc.Systems and methods for quantifying qualitative medical expressions
US6253186 *14 août 199626 juin 2001Blue Cross Blue Shield Of South CarolinaMethod and apparatus for detecting fraud
US6259890 *27 mars 199710 juil. 2001Educational Testing ServiceSystem and method for computer based test creation
US6266645 *1 sept. 199824 juil. 2001Imetrikus, Inc.Risk adjustment tools for analyzing patient electronic discharge records
US6272472 *29 déc. 19987 août 2001Intel CorporationDynamic linking of supplier web sites to reseller web sites
US6322502 *29 déc. 199727 nov. 2001Imd Soft Ltd.Medical information system
US6322504 *27 mars 200027 nov. 2001R And T, LlcComputerized interactive method and system for determining a risk of developing a disease and the consequences of developing the disease
US6338042 *10 juil. 19988 janv. 2002Siemens Information And Communication Networks, Inc.Method and apparatus for integrating competency measures in compensation decisions
US6381576 *16 déc. 199830 avr. 2002Edward Howard GilbertMethod, apparatus, and data structure for capturing and representing diagnostic, treatment, costs, and outcomes information in a form suitable for effective analysis and health care guidance
US6484144 *15 sept. 199919 nov. 2002Dental Medicine International L.L.C.Method and system for healthcare treatment planning and assessment
US6523019 *28 oct. 199918 févr. 2003Choicemaker Technologies, Inc.Probabilistic record linkage model derived from training data
US6551243 *5 juil. 200122 avr. 2003Siemens Medical Solutions Health Services CorporationSystem and user interface for use in providing medical information and health care delivery support
US6551266 *28 déc. 199922 avr. 2003Occulogix CorporationRheological treatment methods and related apheresis systems
US6611846 *30 oct. 199926 août 2003Medtamic HoldingsMethod and system for medical patient data analysis
US6641532 *7 août 20014 nov. 2003First Opinion CorporationComputerized medical diagnostic system utilizing list-based processing
US6645959 *8 nov. 200011 nov. 2003Warner-Lambert CompanyMethod for treating postoperative ileus
US6754655 *12 nov. 199822 juin 2004Simulconsult, Inc.Systems and methods for diagnosing medical conditions
US6802810 *21 sept. 200112 oct. 2004Active Health ManagementCare engine
US6804656 *18 nov. 199912 oct. 2004Visicu, Inc.System and method for providing continuous, expert network critical care services from a remote location(s)
US6826536 *22 juil. 200030 nov. 2004Bert FormanHealth care billing monitor system for detecting health care provider fraud
US6839678 *11 févr. 19994 janv. 2005Siemens AktiengesellschaftComputerized system for conducting medical studies
US6903194 *24 sept. 19977 juin 2005Chungai Seiyaku Kabushiki KaishaAntibody against human parathormone related peptides
US6915254 *30 juil. 19995 juil. 2005A-Life Medical, Inc.Automatically assigning medical codes using natural language processing
US6915266 *31 juil. 20005 juil. 2005Aysha SaeedMethod and system for providing evaluation data from tracked, formatted administrative data of a service provider
US6988075 *15 mars 200017 janv. 2006Hacker L LeonardPatient-controlled medical information system and method
US7058658 *28 mars 20016 juin 2006Dana-Farber Cancer Institute, Inc.Molecular database for antibody characterization
US7130457 *17 juil. 200131 oct. 2006Accuimage Diagnostics Corp.Systems and graphical user interface for analyzing body images
US7307543 *26 sept. 200511 déc. 2007Visicu, Inc.System and method for video observation of a patient in a health care location
US20010011243 *20 mars 20012 août 2001Ron DemboRisk management system, distributed framework and method
US20010023419 *14 août 199720 sept. 2001Jerome LapointeMethod for selecting medical and biochemical diagnostic tests using neural network-related applications
US20010032195 *19 déc. 200018 oct. 2001Graichen Catherine MarySystem and method for identifying productivity improvements in a business organization
US20010051882 *30 mars 200113 déc. 2001Murphy Kevin M.Integrated care management system
US20020002474 *8 août 20013 janv. 2002Michelson Leslie DennisSystems and methods for selecting and recruiting investigators and subjects for clinical studies
US20020010597 *17 mai 200124 janv. 2002Mayer Gregg L.Systems and methods for electronic health management
US20020026322 *28 févr. 200128 févr. 2002John WrightCustomer controlled manufacturing process and user interface
US20020026332 *18 juil. 200128 févr. 2002Snowden Guy B.System and method for automated creation of patient controlled records
US20020032581 *1 juin 200114 mars 2002Reitberg Donald P.Single-patient drug trials used with accumulated database: risk of habituation
US20020035316 *30 août 200121 mars 2002Healtheheart, Inc.Patient analysis and risk reduction system and associated methods
US20020077853 *14 sept. 200120 juin 2002Kevin BoruSystem for selecting clinical trials
US20020082480 *29 août 200127 juin 2002Riff Kenneth M.Medical device systems implemented network scheme for remote patient management
US20020087361 *2 mars 20014 juil. 2002Homeopt LlcHealth care data manipulation and analysis system
US20020099570 *23 août 200125 juil. 2002Knight Stephen C.Recruiting a patient into a clinical trial
US20020123905 *13 déc. 20005 sept. 2002Joane GoodroeClinical operational and gainsharing information management system
US20020138492 *16 nov. 200126 sept. 2002David KilData mining application with improved data mining algorithm selection
US20020138524 *19 janv. 200126 sept. 2002Ingle David BlakemanSystem and method for creating a clinical resume
US20020143577 *2 avr. 20013 oct. 2002Saul ShiffmanApparatus and method for prediction and management of subject compliance in clinical research
US20020165736 *29 août 20017 nov. 2002Jill TolleSystem and methods for generating physician profiles concerning prescription therapy practices
US20020173990 *14 mai 200221 nov. 2002Dominic A. MarascoSystem and method for managing interactions between healthcare providers and pharma companies
US20020177759 *5 sept. 200128 nov. 2002Ido SchoenbergMedical information text display system
US20030028401 *17 juil. 20016 févr. 2003Leon KaufmanCustomizable lung report generator
US20030046114 *23 oct. 20016 mars 2003Davies Richard J.System, method, and apparatus for storing, retrieving, and integrating clinical, diagnostic, genomic, and therapeutic data
US20030050794 *7 sept. 200113 mars 2003Marjorie KeckHospital emergency department resource utilization and optimization system
US20030108938 *6 nov. 200212 juin 2003David PickarPharmacogenomics-based clinical trial design recommendation and management system and method
US20030120133 *4 nov. 200226 juin 2003Rao R. BharatPatient data mining for lung cancer screening
US20030120134 *4 nov. 200226 juin 2003Rao R. BharatPatient data mining for cardiology screening
US20030120458 *4 nov. 200226 juin 2003Rao R. BharatPatient data mining
US20030120514 *4 nov. 200226 juin 2003Rao R. BharatPatient data mining, presentation, exploration, and verification
US20030125984 *4 nov. 20023 juil. 2003Rao R. BharatPatient data mining for automated compliance
US20030125985 *4 nov. 20023 juil. 2003Rao R. BharatPatient data mining for quality adherence
US20030126101 *4 nov. 20023 juil. 2003Rao R. BharatPatient data mining for diagnosis and projections of patient states
US20030130871 *4 nov. 200210 juil. 2003Rao R. BharatPatient data mining for clinical trials
US20030208382 *5 juil. 20016 nov. 2003Westfall Mark DElectronic medical record system and method
US20040078216 *12 avr. 200222 avr. 2004Gregory TotoClinical trial process improvement method and system
US20050187794 *25 janv. 200525 août 2005Alean KimakElectronic medical record registry including data replication
US20060064415 *17 juin 200223 mars 2006Isabelle GuyonData mining platform for bioinformatics and other knowledge discovery
Référencé par
Brevet citant Date de dépôt Date de publication Déposant Titre
US7152785 *9 déc. 200326 déc. 2006Ge Medical Systems Global Technology Company, LlcPatient-centric data acquisition protocol selection and identification tags therefor
US71813754 nov. 200220 févr. 2007Siemens Medical Solutions Usa, Inc.Patient data mining for diagnosis and projections of patient states
US72442306 nov. 200317 juil. 2007Siemens Medical Solutions Usa, Inc.Computer aided diagnostic assistance for medical imaging
US7571191 *5 avr. 20044 août 2009Sap AgDefining a data analysis process
US76170784 nov. 200210 nov. 2009Siemens Medical Solutions Usa, Inc.Patient data mining
US7624098 *7 oct. 200524 nov. 2009International Business Machines CorporationGenerating suitable data for statistical analysis
US77114044 nov. 20024 mai 2010Siemens Medical Solutions Usa, Inc.Patient data mining for lung cancer screening
US77445404 nov. 200229 juin 2010Siemens Medical Solutions Usa, Inc.Patient data mining for cardiology screening
US78490485 juil. 20057 déc. 2010Clarabridge, Inc.System and method of making unstructured data available to structured data analysis tools
US78490495 juil. 20057 déc. 2010Clarabridge, Inc.Schema and ETL tools for structured and unstructured data
US79173774 nov. 200229 mars 2011Siemens Medical Solutions Usa, Inc.Patient data mining for automated compliance
US79746816 juil. 20055 juil. 2011Hansen Medical, Inc.Robotic catheter system
US797653919 juil. 200512 juil. 2011Hansen Medical, Inc.System and method for denaturing and fixing collagenous tissue
US812673623 janv. 200928 févr. 2012Warsaw Orthopedic, Inc.Methods and systems for diagnosing, treating, or tracking spinal disorders
US82142244 nov. 20023 juil. 2012Siemens Medical Solutions Usa, Inc.Patient data mining for quality adherence
US82142254 nov. 20023 juil. 2012Siemens Medical Solutions Usa, Inc.Patient data mining, presentation, exploration, and verification
US828075014 mai 20102 oct. 2012Siemens Medical Solutions Usa, Inc.Patient data mining for cardiology screening
US82928071 sept. 201023 oct. 2012Welch Allyn, Inc.Mobile medical workstation
US839215213 août 20085 mars 2013Siemens Medical Solutions Usa, Inc.Early detection of disease outbreak using electronic patient data to reduce public health threat from bio-terrorism
US86826936 juin 200725 mars 2014Siemens Medical Solutions Usa, Inc.Patient data mining for lung cancer screening
US868509323 janv. 20091 avr. 2014Warsaw Orthopedic, Inc.Methods and systems for diagnosing, treating, or tracking spinal disorders
US875149528 sept. 201010 juin 2014Siemens Medical Solutions Usa, Inc.Automated patient/document identification and categorization for medical data
US894907919 juin 20093 févr. 2015Siemens Medical Solutions Usa, Inc.Patient data mining
US89490826 juin 20113 févr. 2015Siemens Medical Solutions Usa, Inc.Healthcare information technology system for predicting or preventing readmissions
US912905419 sept. 20138 sept. 2015DePuy Synthes Products, Inc.Systems and methods for surgical and interventional planning, support, post-operative follow-up, and, functional recovery tracking
US94777491 mars 201325 oct. 2016Clarabridge, Inc.Apparatus for identifying root cause using unstructured data
US20030120133 *4 nov. 200226 juin 2003Rao R. BharatPatient data mining for lung cancer screening
US20030120134 *4 nov. 200226 juin 2003Rao R. BharatPatient data mining for cardiology screening
US20030120458 *4 nov. 200226 juin 2003Rao R. BharatPatient data mining
US20030120514 *4 nov. 200226 juin 2003Rao R. BharatPatient data mining, presentation, exploration, and verification
US20030125984 *4 nov. 20023 juil. 2003Rao R. BharatPatient data mining for automated compliance
US20030125985 *4 nov. 20023 juil. 2003Rao R. BharatPatient data mining for quality adherence
US20030126101 *4 nov. 20023 juil. 2003Rao R. BharatPatient data mining for diagnosis and projections of patient states
US20040147840 *6 nov. 200329 juil. 2004Bhavani DuggiralaComputer aided diagnostic assistance for medical imaging
US20040186357 *19 août 200323 sept. 2004Welch Allyn, Inc.Diagnostic instrument workstation
US20050027683 *5 avr. 20043 févr. 2005Marcus DillDefining a data analysis process
US20050121505 *9 déc. 20039 juin 2005Metz Stephen W.Patient-centric data acquisition protocol selection and identification tags therefor
US20050203776 *15 mars 200415 sept. 2005Godwin Sharen A.Method of identifying clinical trial participants
US20050288571 *17 mai 200529 déc. 2005Welch Allyn, Inc.Mobile medical workstation
US20060265253 *17 mai 200623 nov. 2006Rao R BPatient data mining improvements
US20070083495 *7 oct. 200512 avr. 2007International Business Machines CorporationSystem and method for generating suitable data for statistical analysis
US20070120640 *28 nov. 200531 mai 2007Han-Ming LeePlug with overload protection and a safety switch
US20070294112 *17 nov. 200620 déc. 2007General Electric CompanySystems and methods for identification and/or evaluation of potential safety concerns associated with a medical therapy
US20070294113 *17 nov. 200620 déc. 2007General Electric CompanyMethod for evaluating correlations between structured and normalized information on genetic variations between humans and their personal clinical patient data from electronic medical patient records
US20080154642 *21 déc. 200626 juin 2008Susan MarbleHealthcare Core Measure Tracking Software and Database
US20090076851 *13 août 200819 mars 2009Siemens Medical Solutions Usa, Inc.Early detection of disease outbreak using electronic patient data to reduce public health threat from bio-terrorism
US20090254376 *6 avr. 20098 oct. 2009The Quantum Group, Inc.Dynamic integration of disparate health-related processes and data
US20100010320 *7 juil. 200914 janv. 2010Perkins David GMobile medical workstation and a temporarily associating mobile computing device
US20100081941 *21 déc. 20071 avr. 2010Endothelix, Inc.Cardiovascular health station methods and apparatus
US20100222646 *14 mai 20102 sept. 2010Siemens Medical Solutions Usa, Inc.Patient Data Mining for Cardiology Screening
US20100312798 *8 déc. 20089 déc. 2010Koninklijke Philips Electronics N.V.Retrieval of similar patient cases based on disease probability vectors
US20110054289 *26 août 20103 mars 2011Adidas AG, World of SportsPhysiologic Database And System For Population Modeling And Method of Population Modeling
US20110078145 *28 sept. 201031 mars 2011Siemens Medical Solutions Usa Inc.Automated Patient/Document Identification and Categorization For Medical Data
US20110184761 *25 janv. 201128 juil. 2011Siemens Medical Solutions Usa, Inc.Method and Apparatus for Estimating Patient Populations
Classifications
Classification aux États-Unis705/3, 707/999.003, 707/999.104
Classification internationaleG06F19/00, G06Q10/00, A61B5/00, G06F17/30
Classification coopérativeG06F19/325, G06F19/3443, G06F19/3418, G06F19/3406, G06F19/324, G06Q10/10, G06F19/327, G06F19/3481, G06Q50/24, G06F19/345, G06F19/3487, G06Q50/22, G06F17/3061, G06F19/321, Y10S128/92, G06F19/363, G06F19/3437, G06F19/3431, G06F19/328, G06F19/322
Classification européenneG06Q10/10, G06F19/34J, G06F19/32E, G06F19/32C, G06F19/34H, G06F19/34G, G06F19/34N, G06F19/36A, G06Q50/24, G06F17/30T, G06F19/32E1, G06Q50/22, G06F19/32G, G06F19/34P
Événements juridiques
DateCodeÉvénementDescription
25 févr. 2003ASAssignment
Owner name: SIEMENS MEDICAL SOLUTIONS, INC., PENNSYLVANIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:RAO, R. BHARAT;REEL/FRAME:013792/0073
Effective date: 20030206
Owner name: SIEMENS CORPORATE RESEARCH, INC., NEW JERSEY
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SANDILYA, SATHYAKAMA;REEL/FRAME:013792/0080
Effective date: 20030206
25 août 2003ASAssignment
Owner name: SIEMENS MEDICAL SOLUTIONS USA, INC., PENNSYLVANIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:RAO, R. BHARAT;REEL/FRAME:014422/0972
Effective date: 20030206
7 juin 2010ASAssignment
Owner name: SIEMENS CORPORATION,NEW JERSEY
Free format text: MERGER;ASSIGNOR:SIEMENS CORPORATE RESEARCH, INC.;REEL/FRAME:024493/0173
Effective date: 20091001
Owner name: SIEMENS CORPORATION, NEW JERSEY
Free format text: MERGER;ASSIGNOR:SIEMENS CORPORATE RESEARCH, INC.;REEL/FRAME:024493/0173
Effective date: 20091001
10 déc. 2014ASAssignment
Owner name: SIEMENS MEDICAL SOLUTIONS USA, INC., PENNSYLVANIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SIEMENS CORPORATION;REEL/FRAME:034446/0566
Effective date: 20141121
6 févr. 2015ASAssignment
Owner name: CERNER INNOVATION, INC., KANSAS
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SIEMENS MEDICAL SOLUTIONS USA, INC.;REEL/FRAME:034914/0523
Effective date: 20150202